To describe the distribution of points in a cell, we employ the normal distribution, \(C\sim (\mu , \Sigma )\) , where \(\mu \)  is the mean and \(\Sigma \)  is the covariance of points in a cell, to encode the local shape[1]}. In our approach, we describe all the points in one cell with two statistics of a normal distribution. The first is probability density score:
\(P=\sum _{i}^{N} \exp \left(-\frac{\left(x_{i}-\mu \right)^{\prime } \Sigma ^{-1}\left(x_{i}-\mu \right)}{2}\right),\) 
